Studies correlate higher overall customer satisfaction levels with improved profitability for business organizations. This correlation may be explained by 1) a satisfied customer is more likely to solicit future business from an organization; and 2) a satisfied customer is more likely to recommend an organization's offerings to their acquaintances, which provides opportunities for acquiring new business.
Today, a large number of business organizations constantly survey a sample of their customers in order to quantitatively project an overall customer satisfaction level. This metric can be thought of as a “customer pulse” By being sensitive to variations and trending patterns in the value of such a metric over time, on organization can react quickly to address areas of customer pain or to faster adjust to shifting customer expectations.
In order for an organization to apply appropriate remediative adjustments, it is critical to be able to associate and explain a specific variation (e.g. an unexpected drop in overall customer satisfaction) against tangible causal factors.
An important resource for evaluating meaningful cause behind shifting overall customer satisfaction is direct customer feedback (e.g. solicited customer surveys and direct customer complaints) and indirect customer feedback (e.g. feedback garnered from social media channels). Such feedback is typically collected as unstructured text.
Conventional approaches to evaluating causal cues from unstructured text require human resources to physically read all feedback associated with the variation, and to then make inferences on which specific issues may have caused the variation. Such an approach is time-consuming, and any delay in identifying issues may translate to loss of potential revenue. Conventional approaches are also labor intensive, inconsistent, error-prone, and tend to be influenced by subjective judgment.
Various embodiments include systems and methods for automating causal analysis.